pyiqa.archs.qualiclip_arch
==========================

.. py:module:: pyiqa.archs.qualiclip_arch

.. autoapi-nested-parse::

   Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment

   @article{agnolucci2024qualityaware,
         title={Quality-Aware Image-Text Alignment for Real-World Image Quality Assessment},
         author={Agnolucci, Lorenzo and Galteri, Leonardo and Bertini, Marco},
         journal={arXiv preprint arXiv:2403.11176},
         year={2024}
   }

   Reference:
       - Arxiv link: https://arxiv.org/abs/2403.11176
       - Official Github: https://github.com/miccunifi/QualiCLIP







Module Contents
---------------

.. py:data:: default_model_urls

.. py:class:: PromptLearner(clip_model, prompt_pairs, n_ctx=16, ctx_init='', prompt_specific_ctx=False)

   Bases: :py:obj:`torch.nn.Module`


   PromptLearner class for learning prompts for QualiCLIP+. See https://github.com/IceClear/CLIP-IQA for reference.


   .. py:method:: get_prompts_with_middle_class()

      Get prompts with the original text embedding inserted in the middle.

      :returns: The generated prompts.
      :rtype: torch.Tensor



   .. py:method:: forward(clip_model)

      Forward pass for the PromptLearner.

      :param clip_model: The CLIP model.
      :type clip_model: nn.Module

      :returns: The output features.
      :rtype: torch.Tensor



.. py:class:: QualiCLIP(model_type='qualiclip+', backbone='RN50', temperature=2, n_ctx=16, ctx_init='', prompt_specific_ctx=True, pretrained=True, pos_embedding=False)

   Bases: :py:obj:`torch.nn.Module`


   QualiCLIP model implementation following the original paper. QualiCLIP+ represents the version that employs prompt
   learning, similar to CLIP-IQA+ (https://arxiv.org/abs/2207.12396).


   .. py:method:: forward(x)